Interactive indoor navigation framework

ABSTRACT

According to an aspect of an embodiment, operations include receiving user information comprising motion information associated with a group of users in a built environment and user preference information associated with a group of events hosted in the built environment. The operations further include receiving layout information associated with the built environment and schedule information associated with the group of events. The operations further include predicting event-specific action associated with each user based on the user information, and the layout and schedule information. The operations further include predicting a path for navigation towards an event location associated with one of the group of events based on the predicted event-specific action and historical movement information of the group of users. The operations further include selecting, from the predicted paths, one or paths for navigation and generating navigation suggestions based on the selected paths. The navigation suggestions are displayed on display devices.

FIELD

The embodiments discussed in the present disclosure are related to aninteractive indoor navigation framework.

BACKGROUND

With surging populations and growing demands, traversing through crowdshas become more common than ever before. Indoor navigation allows peopleto navigate indoors, typically within buildings, such as hotels ormarket areas. As satellite-based navigation is almost non-existent in anindoor environment, other positioning methods may be used to enableindoor navigation for people looking for accurate navigation toparticular locations of the indoor environment. Indoor navigation hasmany applications for personal navigation, commercial, retail, military,logistics, or other location-based industries. Common applications ofindoor navigation can be found in robotic navigation inside facilities,like a warehouse or hospitals, or turn-by-turn navigation for individualusers to navigate on train stations, airports, shopping centers,museums, or conference centers.

The subject matter claimed in the present disclosure is not limited toembodiments that solve any disadvantages or that operate only inenvironments such as those described above. Rather, this background isonly provided to illustrate one example technology area where someembodiments described in the present disclosure may be practiced.

SUMMARY

According to an aspect of the disclosure, operations may includereceiving user information that may indicate motion informationassociated with a group of users in a built environment and userpreference information associated with a group of events hosted in thebuilt environment. The operations may further include receiving layoutinformation that may be associated with the built environment andreceiving schedule information that may be associated with the group ofevents. The operations may further include predicting an event-specificaction associated with each user of the group of users in the builtenvironment based on the received user information, the received layoutinformation, the received schedule information. The operations mayfurther include predicting, for each user of the group of users, a pathfor navigation towards a first event location associated with one of thegroup of events based on the predicted event-specific action of eachuser of the group of users and historical movement information of thegroup of users inside the built environment. The operations may furtherinclude selecting, from the predicted paths for the group of users, oneor more paths as one or more optimal options for navigation towards thefirst event location. The operations may further include generating oneor more navigation suggestions based on the selected one or more pathsand controlling one or more display devices to display the generated oneor more navigation suggestions.

The objects and advantages of the embodiments will be realized andachieved at least by the elements, features, and combinationsparticularly pointed out in the claims.

Both the foregoing general description and the following detaileddescription are given as examples and are explanatory and are notrestrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1 is a diagram representing an example environment related toproviding navigation suggestions in a built environment,

FIG. 2 is a block diagram of a system for providing navigationsuggestions in a built environment,

FIG. 3 is a diagram that depicts exemplary operations for providingnavigation suggestions in a built environment, and

FIG. 4 is a flowchart of an example method of providing navigationsuggestions in a built environment,

all according to at least one embodiment described in the presentdisclosure.

DESCRIPTION OF EMBODIMENTS

Some embodiments described in the present disclosure relate to methodsand systems for providing an interactive indoor navigation framework fornavigation in indoor environments. With surging populations and growingdemands, traversing through crowds has become more common than everbefore. Conventional navigation solutions, such as web mapping servicesrely on various sources of location data, such as Global NavigationSatellite Systems (GNSS) receivers, mobile networks, or Wi-Fi forproviding real-time traffic conditions and route planning for travelingby foot, car, bicycle, air, or public transportation. Such solutions aretypically effective in navigating between two distant locations;however, they are not built for navigation in indoor environments, suchas conference centers or outdoor market areas. This may be because theseconventional solutions rely on location data which lacks precision orfails entirely inside indoor environments, such as multistory buildings,airports, alleys, parking garages, or underground locations.

Indoor navigation has many applications for personal navigation,commercial, retail, military, logistics, or other location-basedindustries. Typically, people need indoor navigation to find andnavigate to a desired location in an indoor environment, such as aconference room, a restroom, or a banquet hall in a building.Conventional indoor navigation solutions typically rely on technologies,such as Bluetooth® beacon-based systems, receiver antenna array systems,Wi-Fi based systems, short-range radio signals, and ultra-widebandpositioning systems.

As one example, multiple Bluetooth® low energy (BLE) beacons may be usedfor navigating in the indoor environment. Such beacons may be registeredto a location in the indoor environment and may regularly broadcasttheir location information to nearby user devices, which may analyze thelocation information to assist the user in finding right directions todesired location in the indoor environment. As another example,applications for the indoor navigation, especially for roboticnavigation, may implement simultaneously localization and mapping(SLAM). SLAM may refer to a computational problem of constructing orupdating a map of an unknown environment while simultaneously keepingtrack of an agent's location within it. It uses approximation solutionmethods, such as a Kalman filter or a particle filter to track andupdate the location of the agent. Most of these conventional solutionsfocus on adapting the agent in navigating the environment and not onadapting the environment to aid in navigation.

According to one or more embodiments of the present disclosure, thetechnological field of indoor navigation system may be improved byconfiguring a system in a manner in which the system is able to providean efficient solution for pedestrian navigation within the indoorenvironment. The solution may include collecting user informationincluding motion information associated with a group of users in a builtenvironment and user preference information associated with a group ofevents hosted in the built environment. Layout information associatedwith the built environment and schedule information associated with thegroup of events may also be collected. The solution may includepredicting an event-specific action for each of the group of users basedon the collected information. Based on the predicted event-specificaction and historical movement information of the group of users, a pathfor each user may be predicted for navigation towards an event locationassociated with one of the group of events. From the predicted paths,the solution may include selecting one or more paths as optimal optionsfor navigation towards the event location. Based on the selected one ormore paths, suitable navigation suggestions may be generated anddisplayed on display devices in the built environment.

Contrary to conventional solutions, the disclosed solution factors inuser's motion tracker information, user's event schedule, user'sevent-specific preferences, and layout information of the indoorenvironment to predict user's movement and provide suitable navigationsuggestions inside the indoor environment. The solution may be usefulfor people who typically rely on structural layout maps of the indoorenvironment to navigate in the indoor environment and for people with adisability (e.g. visual impairment).

Conventional event planning apps focus on logistical management. such asfor event planning, task creation, seating plan, marketing, programcreation/scheduling, integrated calendars, chat application, ormaintaining attendee database. Such applications don't provide aninterface that aids in navigation, especially in indoor environment. Incontrast, the present disclosure provides to fuse information containedin the event along with data about people's movement, their preferencesto aid in navigation by adapting the environment.

Embodiments of the present disclosure are explained with reference tothe accompanying drawings.

FIG. 1 is a diagram representing an example environment related toproviding navigation suggestions in a built environment, arranged inaccordance with at least one embodiment described in the presentdisclosure. With reference to FIG. 1, there is shown an environment 100.The environment 100 includes a system 102, a plurality of motiontrackers 104, one or more display devices 106, and a built environment108. The system 102, the plurality of motion trackers 104, and the oneor more display devices 106 may be communicatively coupled to eachother, via a communication network 110. In FIG. 1, there is furthershown a group of users 112A, 112B, 112C and 112D in the builtenvironment 108. The built environment 108 may refer to anypredetermined place and space created or modified by people to servetheir needs of accommodation, organization, or representation. As shown,for example, the built environment 108 is a multi-story office building.However, the disclosure may not be so limiting and may be applicable toother built environments. Examples of the built environment 108 mayinclude, but are not limited to, a single-story building, a multi-storybuilding, a conference building, an outdoor market area, a shoppingmall, a theme park, a hotel, a theater, a stadium, an airport, a school,a park, or a hospital.

In one or more embodiments, the plurality of motion trackers 104 and theone or more display devices 106 may be located at a plurality oflocations within the built environment 108. In these or otherembodiments, one or more of the plurality of motion trackers 104 and theone or more display devices 106 may be associated with the group ofusers 112A, 112B, 112C, and 112D. For example, a user device (forexample, a smartphone or a smartwatch) may act as a motion tracker and adisplay device for one of the group of users 112A, 112B, 112C, and 112D.

The system 102 may include suitable logic, circuitry, and interfacesthat may be configured to receive user information associated with thegroup of users 112A, 112B, 112C, and 112D in the built environment 108.The received user information may include motion information associatedwith the group of users 112A, 112B, 112C, and 112D. In an embodiment,the system 102 may control the plurality of motion trackers 104 at acorresponding plurality of locations inside the built environment 108 tocollect the motion information associated with the group of users 112A,112B, 112C, and 112D. The plurality of motion trackers 104 may include agroup of homogenous sensors or a group of sensors which act as disparatesources of the motion information. In an exemplary embodiment, each ofthe plurality of motion trackers 104 may be implemented as animage-capture device, such as a Closed-Circuit Television (CCTV) camera.Other example implementations of a motion tracker may include, but arenot limited to, ultrasonic beacons, NFC/Bluetooth®, a depth sensor, aLight Detection and Ranging (LiDAR) sensor, a Radar sensor, an InertialMeasurement Unit (IMU), a user device with a capability to access acellular network or a wireless local area network (WLAN), a GlobalNavigation Satellite System (GNSS) receiver, an Infrared (IR)sensor/Passive-IR sensor, an accelerometer, a gyroscope, or othersensors with ability to track human motion.

The received user information may also include user preferenceinformation associated with a group of events hosted in the builtenvironment 108. Herein, each event may refer to an occasion which maybe planned and scheduled to take place at a particular date-time and/orat a particular location in the built environment 108. For each user,the user preference information may include a user preference for one ormore events of the group of events hosted in the built environment 108.Further details associated with the motion information and the userpreference information are provided, for example, in FIG. 3.

The system 102 may further receive layout information associated withthe built environment 108 and schedule information associated with thegroup of events. The schedule information may include an event location(e.g., a floor number, a hall number or name, and the like) anddate-time information at which each event of the group of events maytake place in the built environment 108. For example, the scheduleinformation for a poster presentation event in a university campus (i.e.the built environment 108) may include an event location in terms of abuilding identifier (e.g., Tower 3), a floor number (e.g., 2^(nd)), or ahall number/name (e.g., 202). The schedule information may also includedate-time information (e.g., 23 Mar. 2020, 12:30 PM) associated witheach event of the group of events. Further details on the scheduleinformation and the layout information are provided, for example, inFIG. 3.

Based on the received user information, the received layout information,and the received schedule information, the system 102 may predict anevent-specific action associated with each user of the group of users112A, 112B, 112C, and 112D in the built environment 108. Theevent-specific action may be predicted from an action space which mayinclude an exhaustive list of all possible event-specific actions. Inone or more embodiments, for each user of the group of users 112A, 112B,112C, and 112D, the event-specific action may indicate a maximumlikelihood of movement of the corresponding user towards a location inthe built environment 108. For example, an event-specific action mayindicate that a movement of the user 112D towards a conference room.

The system 102 may predict, for each user of the group of users 112A,112B, 112C, and 112D, a path for navigation towards a first eventlocation associated with one of the group of events. Such prediction maybe based on the predicted event-specific action of each user of thegroup of users 112A, 112B, 112C, and 112D and historical movementinformation of the group of users 112A, 112B, 112C, and 112D inside thebuilt environment 108. Details associated with prediction of theevent-specific action and the path for navigation are provided, forexample, in FIG. 3.

From the predicted paths for the group of users 112A, 112B, 112C, and112D, the system 102 may select one or more paths as one or more optimaloptions for navigation towards the first event location. Thereafter, thesystem 102 may generate one or more navigation suggestions based on theselected one or more paths. For example, if a selected path for the user112D includes the use of an elevator to move from a ground floor to thefirst floor, then a navigation suggestion may be generated as “take thelift on the ground floor to move to first floor”. Details of pathselection and generation of navigation suggestions are provided, forexample, in FIG. 3.

The system 102 may control the one or more display devices 106 todisplay the generated one or more navigation suggestions. In anembodiment, the one or more display devices 106 may be located at one ormore locations in the built environment 108 to allow the group of users112A, 112B, 112C, and 112D to view the generated one or more navigationsuggestions. In another embodiment, the one or more display devices 106may be personal user devices which may display personalized navigationsuggestion to each user of the group of users 112A, 112B, 112C, and112D. The one or more display devices 106 may be realized throughseveral known technologies such as, but not limited to, at least one ofa Liquid Crystal Display (LCD) display, a Light Emitting Diode (LED)display, a plasma display, or an Organic LED (OLED) display technology,or other display devices.

It should be noted that the communication among the system 102, theplurality of motion trackers 104, and the one or more display devices106 may be performed via the communication network 110. Thecommunication network 110 may include a communication medium throughwhich the system 102 may communicate with the plurality of motiontrackers 104, the one or more display devices 106, or othercommunication devices. Details of such communication devices are omittedfrom the disclosure for the sake of brevity. Examples of thecommunication network 110 may include, but are not limited to, theInternet, a cloud network, a Wireless Fidelity (Wi-Fi) network, aPersonal Area Network (PAN), a Local Area Network (LAN), and/or aMetropolitan Area Network (MAN). Various devices in the environment 100may be configured to connect to the communication network 110, inaccordance with various wired and wireless communication protocols.Examples of such wired and wireless communication protocols may include,but are not limited to, at least one of a Transmission Control Protocoland Internet Protocol (TCP/IP), User Datagram Protocol (UDP), HypertextTransfer Protocol (HTTP), File Transfer Protocol (FTP), ZigBee, EDGE,IEEE 802.11, light fidelity (Li-Fi), 802.16, IEEE 802.11s, IEEE 802.11g,multi-hop communication, wireless access point (AP), device to devicecommunication, cellular communication protocols, and/or Bluetooth (BT)communication protocols, or a combination thereof.

In FIG. 1, the plurality of motion trackers 104 and the one or moredisplay devices 106 are shown as two separate entities from the system102. However, in certain exemplary embodiments, the entire functionalityof the plurality of motion trackers 104 and the one or more displaydevices 106 may be incorporated in the system 102, without deviatingfrom the scope of the disclosure.

FIG. 2 is a block diagram of a system for providing navigationsuggestions in a built environment, arranged in accordance with at leastone embodiment described in the present disclosure. FIG. 2 is explainedin conjunction with elements from FIG. 1. With reference to FIG. 2,there is shown a block diagram 200 of the system 102. The system 102 mayinclude a processor 202, a memory 204, a persistent data storage 206, aninput/output (I/O) device 208, and a network interface 210. In one ormore embodiments, the system 102 may also include the plurality ofmotion trackers 104.

The processor 202 may include suitable logic, circuitry, and/orinterfaces that may be configured to execute program instructionsassociated with different operations to be executed by the system 102.The processor 202 may include any suitable special-purpose orgeneral-purpose computer, computing entity, or processing deviceincluding various computer hardware or software modules and may beconfigured to execute instructions stored on any applicablecomputer-readable storage media. For example, the processor 202 mayinclude a microprocessor, a microcontroller, a digital signal processor(DSP), an application-specific integrated circuit (ASIC), aField-Programmable Gate Array (FPGA), or any other digital or analogcircuitry configured to interpret and/or to execute program instructionsand/or to process data. Although illustrated as a single processor inFIG. 2, the processor 202 may include any number of processorsconfigured to, individually or collectively, perform or directperformance of any number of operations of the system 102, as describedin the present disclosure. Additionally, one or more of the processorsmay be present on one or more different electronic devices, such asdifferent servers.

In some embodiments, the processor 202 may be configured to interpretand/or execute program instructions and/or process data stored in thememory 204 and/or the persistent data storage 206. In some embodiments,the processor 202 may fetch program instructions from the persistentdata storage 206 and load the program instructions in the memory 204.After the program instructions are loaded into memory 204, the processor202 may execute the program instructions. Some of the examples of theprocessor 202 may be a GPU, a CPU, a RISC processor, an ASIC processor,a CISC processor, a co-processor, and/or a combination thereof.

The memory 204 may include suitable logic, circuitry, and/or interfacesthat may be configured to store program instructions executable by theprocessor 202. In one or more embodiments, the memory 204 may store userinformation including the motion information associated with the groupof users 112A, 112B, 112C, and 112D in the built environment 108 and theuser preference information associated with the group of events. In oneor more embodiments, the memory 204 may also store the layoutinformation associated with the built environment 108 and the scheduleinformation associated with the group of events. In one or moreembodiments, the memory 204 may also store the historical movementinformation of the group of users 112A, 112B, 112C, and 112D inside thebuilt environment 108. The memory 204 may include computer-readablestorage media for carrying or having computer-executable instructions ordata structures stored thereon. Such computer-readable storage media mayinclude any available media that may be accessed by a general-purpose orspecial-purpose computer, such as the processor 202.

By way of example, and not limitation, such computer-readable storagemedia may include tangible or non-transitory computer-readable storagemedia including Random Access Memory (RAM), Read-Only Memory (ROM),Electrically Erasable Programmable Read-Only Memory (EEPROM), CompactDisc Read-Only Memory (CD-ROM) or other optical disk storage, magneticdisk storage or other magnetic storage devices, flash memory devices(e.g., solid state memory devices), or any other storage medium whichmay be used to carry or store particular program code in the form ofcomputer-executable instructions or data structures and which may beaccessed by a general-purpose or special-purpose computer. Combinationsof the above may also be included within the scope of computer-readablestorage media. Computer-executable instructions may include, forexample, instructions and data configured to cause the processor 202 toperform a certain operation or group of operations associated with thesystem 102.

The persistent data storage 206 may include suitable logic, circuitry,and/or interfaces that may be configured to store program instructionsexecutable by the processor 202. The persistent data storage 206 mayinclude computer-readable storage media for carrying or havingcomputer-executable instructions or data structures stored thereon. Suchcomputer-readable storage media may include any available media that maybe accessed by a general-purpose or a special-purpose computer, such asthe processor 202.

By way of example, and not limitation, such computer-readable storagemedia may include tangible or non-transitory computer-readable storagemedia including, but not limited to, Compact Disc Read-Only Memory(CD-ROM) or other optical disk storage, magnetic disk storage or othermagnetic storage devices (e.g., Hard-Disk Drive (HDD)), flash memorydevices (e.g., Solid State Drive (SSD), Secure Digital (SD) card, othersolid state memory devices), or any other storage medium which may beused to carry or store particular program code in the form ofcomputer-executable instructions or data structures and which may beaccessed by a general-purpose or a special-purpose computer.Combinations of the above may also be included within the scope ofcomputer-readable storage media. Computer-executable instructions mayinclude, for example, instructions and data configured to cause theprocessor 202 to perform a certain operation or a group of operationsassociated with the system 102.

The I/O device 208 may include suitable logic, circuitry, interfaces,and/or code that may be configured to receive a user input. The I/Odevice 208 may be further configured to provide an output in response tothe user input. The I/O device 208 may include various input and outputdevices, which may be configured to communicate with the processor 202and other components, such as the network interface 210. Examples of theinput devices may include, but are not limited to, a touch screen, akeyboard, a mouse, a joystick, and/or a microphone. Examples of theoutput devices may include, but are not limited to, a display and aspeaker.

The network interface 210 may comprise suitable logic, circuitry,interfaces, and/or code that may be configured to establish acommunication with the system 102, via the communication network 110.The network interface 210 may be implemented by use of various knowntechnologies to support wired or wireless communication of the system102 via the communication network 110. The network interface 210 mayinclude, but is not limited to, an antenna, a radio frequency (RF)transceiver, one or more amplifiers, a tuner, one or more oscillators, adigital signal processor, a coder-decoder (CODEC) chipset, a subscriberidentity module (SIM) card, and/or a local buffer.

The network interface 210 may communicate via wireless communicationwith networks, such as the Internet, an Intranet and/or a wirelessnetwork, such as a cellular telephone network, a wireless local areanetwork (LAN) and/or a metropolitan area network (MAN). The wirelesscommunication may use any of a plurality of communication standards,protocols and technologies, such as Global System for MobileCommunications (GSM), Enhanced Data GSM Environment (EDGE), widebandcode division multiple access (W-CDMA), Long Term Evolution (LTE), codedivision multiple access (CDMA), time division multiple access (TDMA),Bluetooth, Wireless Fidelity (Wi-Fi) (such as IEEE 802.11a, IEEE802.11b, IEEE 802.11g and/or IEEE 802.11n), voice over Internet Protocol(VoIP), light fidelity (Li-Fi), or Wi-MAX.

Modifications, additions, or omissions may be made to the system 102without departing from the scope of the present disclosure. For example,in some embodiments, the system 102 may include any number of othercomponents that may not be explicitly illustrated or described.

FIG. 3 is a diagram that depicts exemplary operations for providingnavigation suggestions in a built environment, in accordance with atleast one embodiment of the disclosure. FIG. 3 is explained inconjunction with elements from FIG. 1 and FIG. 2. With reference to FIG.3, there is shown a block diagram 300 that illustrates exemplaryoperations from 302 to 310 for providing navigation suggestions. Theexemplary operations may be performed by any computing system, forexample, by the system 102 of FIG. 1 or FIG. 2.

At 302, data acquisition may be performed. For that, the system 102 mayreceive user information 312 associated with the group of users 112A,112B, 112C, and 112D in the built environment 108. The user information312 may be received from various disparate data sources, such as, butnot limited to, user devices (such as smartphones or smart wearables),cameras (such as CCTV or drone cameras), data aggregators (such as thirdparty aggregators) which collect user preferences for events, or othersensors located at multiple locations in the built environment 108. Thereceived user information 312 may include motion information 314associated with the group of users 112A, 112B, 112C, and 112D in thebuilt environment 108 and user preference information 316 associatedwith a group of events hosted in the built environment 108.

The motion information 314 associated with the group of users 112A,112B, 112C, and 112D may be indicative of a user activity and a currentuser location with respect to a known location or space in the builtenvironment 108. In one or more embodiments, the system 102 may controlthe plurality of motion trackers 104 inside the built environment 108 tocollect the motion information 314. By way of example, and notlimitation, each motion tracker may be implemented as an image capturedevice, such as a CCTV camera installed at a particular location in thebuilt environment 108. In such a case, the system 102 may receive, fromthe plurality of motion trackers 104, a plurality of images 314A of thegroup users 302 as the motion information 314. In some instances,multiple image capture devices may be positioned at particular locationsto have a combined field-of-view (FOV) which covers one or morelocations in the built environment 108 from multiple viewpoints. Imagesfrom the multiple viewpoints may be fused to accurately track andidentify movements of the group of users 112A, 112B, 112C, and 112.Examples of the image capture device may include, but are not limitedto, a wide-angle camera, an action camera, a closed-circuit television(CCTV) camera, a camcorder, a digital camera, a camera phone, atime-of-flight camera (ToF camera), a night-vision camera, and/or otherimage capture devices.

For each user, the user preference information 316 may include a userpreference for one or more events of the group of events hosted in thebuilt environment 108. In one or more embodiments, the system 102 mayextract the user preference information 316 from logs of user activityon various user platforms, such as websites, smartphone applications,social media application, or from various data aggregators.Additionally, or alternatively, the user preference information 316 maybe extracted based on information associated with events marked in auser's calendar application. Additionally, or alternatively, the userpreference information 316 may be extracted from a history of pastevents attended by each user of the group of users 112A, 112B, 112C, and112D. Additionally, or alternatively, the user preference information316 may be extracted based on a user input via an application interfacerendered on a display of a user device. The application interface maydisplay a list which includes the group of events to be hosted in thebuilt environment 108 and the user input may include a selection of theone or more events from the displayed list. The selection may indicate auser preference to attend the one or more events.

The system 102 may also receive layout information 318 associated withthe built environment 108. The layout information 318 may include astatic physical layout 318A of the built environment 108 and/or adynamic layout indicative of user density at a plurality of locations inthe built environment 108. In one or more embodiments, the system 102may retrieve the static physical layout 318A of the built environment108 from a server. For example, the server may maintain a repository of2D/3D layouts of various built environments.

For the dynamic layout, the system 102 may estimate occupancyinformation associated with the group of users 112A, 112B, 112C, and112D in the built environment 108 based on the motion information 314.In one or more embodiments, the system 102 may generate a heat map basedon the estimated occupancy information. The generated heat map may beoverlaid onto the static physical layout 318A to generate the dynamiclayout. The heat map, when overlaid, may be indicative of the userdensity at different locations of the built environment 108. The userdensity at different locations may change as individual users move, formgroups, or when such groups divide over time. In other words, as peoplemove around in the built environment 108, the dynamic layout may beupdated as well to display the changes in the user density over timeinside the built environment 108.

The system 102 may also receive schedule information 320 associated withthe group of events. The schedule information 320 may include an eventname for each event of the group of events, an event location of eachevent of the group of events in the built environment 108, a seatingcapacity associated with each event of the group of events, and a timeor duration associated with each event of the group of events. Forexample, the group of events hosted in the built environment 108 may bea conference. The schedule information 320 associated with theconference may include an event name such as “Conference on ArtificialIntelligence”, an event location associated with the conference such as“conference hall 2, second floor”, a seating capacity available for theconference as “200”, and a time or duration associated with theconference such as “from 3 PM-4 PM on 5 Apr. 2020”.

At 304, relational action forecasting may be performed. The system 102may predict an event-specific action associated with each user of thegroup of users 112A, 112B, 112C, and 112D in the built environment 108.Such predictions may be performed based on the received user information312, the received layout information 318, and the received scheduleinformation 320. The event-specific action for each user of the group ofusers 112A, 112B, 112C, and 112D may indicate a maximum likelihood ofmovement of the respective user towards a particular location in thebuilt environment 108. In other words, the event-specific action mayrefer to a most probable action for every person in the builtenvironment 108. For example, possible event-specific actions for a usermay be visiting a conference hall or a café in a built environment 108.Based on the received user information 312, the received layoutinformation 318, and the received schedule information 320, it may bepredicted that the user may be most likely heading towards theconference hall to attend an event (e.g., Conference on ArtificialIntelligence).

For prediction of such actions, the system 102 may consider spatial andtemporal interactions between person-person, and person-objects in thebuilt environment 108. In one or more embodiments, the system 102 mayprovide the received user information 312, the received layoutinformation 318, the received schedule information 320 as input to agraph-structured recurrent neural network 322. In an embodiment, each ofthe received user information 312, the received layout information 318,and the received schedule information 320 may be converted into separateembeddings, which may be concatenated together and then provided as theinput to the graph-structured recurrent neural network 322.

The graph-structured recurrent neural network 322 may be a pre-trainedneural network in which nodes may be configured for user detection andedges may be configured to learn spatial or temporal user-to-userinteractions and/or user-to-object interactions in the built environment108. Based on the input, the graph-structured recurrent neural network322 may select an action label for each user of the group of users 112A,112B, 112C, and 112D. For each user, the selected action label may havea maximum likelihood from among a plurality of action labels of anaction space. The system 102 may predict the event-specific action ofeach user of the group of users 112A, 112B, 112C, and 112D in the builtenvironment 108 based on the selected action label for the respectiveuser.

In one or more embodiments, the action space may refer to a collectionof all possible event-specific actions associated with the group ofusers 112A, 112B, 112C, and 112D in the built environment 108. Forexample, in a first scene of a video, a person may be seen reading abook while sitting on a chair. In a subsequent scene, the person may beseen talking to another person standing next to the chair while holdingthe book in hand. The action space for such a scenario may be that theperson may resume reading the book or the person may stand up and leave.Similarly, the action space for event-specific actions may includeaction labels which indicate locations in the built environment 108where a user or a crowd may be heading to. For example, the action spacemay include action labels, such as more people are likely to headtowards poster sessions, few people are preferring invited session 3,continuous flow towards café, or no one is likely to go towards thebanquet hall.

At 306, relational path forecasting may be performed. For each user ofthe group of users 112A, 112B, 112C, and 112D, the system 102 maypredict a path for navigation towards a first event location associatedwith one of the group of events. Herein, the path may include areference to one or more of a location name, a checkpoint, autility/facility (such as elevators, stairs, or escalators), a floornumber, a direction, a sign (e.g., a digital signage), or anyidentifiable structure in the built environment 108. Such prediction maybe performed based on the predicted event-specific action of each userof the group of users 112A, 112B, 112C, and 112D and historical movementinformation 324 of the group of users 112A, 112B, 112C, and 112D insidethe built environment 108. For each user, the historical movementinformation 324 may include a tracked sequence of past locations visitedby the respective user in the built environment 108. For example, forsome users, the predicted event-specific action may indicate an intentto attend a poster presentation session on the first floor of atwo-floor building. The historical movement information 324 may indicatea movement towards a space which leads to both a staircase and anelevator lobby. Based on the historical movement information 324 and thepredicted event-specific actions, the system 102 may predict the pathfor navigation towards the event location of the poster presentationsession. If elevators in the elevator lobby are fairly empty as comparedto the staircase, the predicted path may include the elevators as apotential option to move to the first floor.

In one or more embodiments, the system 102 may provide the predictedevent-specific actions of the group of users 112A, 112B, 112C, and 112Dand the historical movement information 324 of the group of users 112A,112B, 112C, and 112D as input to an imitative decision learningframework 326. The imitative decision learning framework 326 may beconfigured to mimic the human decision-making process to predict thepaths (i.e. probable upcoming paths) in the built environment 108. Inone implementation, the imitative decision learning framework 326 may bebased on Generative Adversarial Imitation Learning (GAIL) and may inferlatent decisions of individual users in the built environment 108 basedon historical observations (i.e. the historical movement information324).

The imitative decision learning framework 326 may extract, using aninference network, a distribution of latent decisions associated withthe group of users 112A, 112B, 112C, and 112D based on the input. Everylatent decision may internally determine a motion pattern of arespective user in the built environment 108. The motion pattern may bean outcome of user's decisions, which may be made upon spatiotemporalinteractions of the user with other users/objects in the builtenvironment 108.

The imitative decision learning framework 326 may generate a policy forpredicting the path for the navigation towards the first event locationfor each user of the group of users 112A, 112B, 112C, and 112D. Forexample, the policy generation may be based on the input and theextracted distribution of latent decisions. The system 102 may predict,for each user of the group of users 112A, 112B, 112C, and 112D, the pathfor the navigation towards the first event location based on thegenerated policy.

At 308, route optimization may be performed. With the routeoptimization, the objective is to select an optimal path which a user ora group of users 112A, 112B, 112C, and 112D can take to reach theirpreferred event location. The system 102 may select, from the predictedpaths (obtained at 306) for the group of users 112A, 112B, 112C, and112D, one or more paths as one or more optimal options for navigationtowards the first event location. For example, if there are many peoplegoing to an event location, the optimal selection of the one or morepaths may depend on a seating capacity of the event location. Thus, ifmany people are heading towards a poster session but the session canonly accommodate 200 people and there is a user who is predicted tofollow a path (such as stairs) to attend the poster session, but given adiminishing rate of the seating capacity at the event location, then anoptimal option/path for the user may be to either use an elevator (whichother users may be opting for) or to skip the poster session to avoidany inconvenience.

In one or more embodiments, the system 102 may determine whether a firstpath of the predicted paths (obtained at 306) for a first user of thegroup of users 112A, 112B, 112C, and 112D coincides with a second pathof the predicted paths for a second user of the group of users 112A,112B, 112C, and 112D. In other words, it may be determined whether pathsof two or more users coincide or overlap. If yes, then such paths may bemerged in order to accommodate a common goal whenever feasible. Thesystem 102 may select the first path as an optimal option for both thefirst user and the second user for the navigation towards the firstevent location.

In these or other embodiments, the system 102 may extract constraintinformation which may include seating capacity information associatedwith the first event location in the built environment 108 based on thereceived layout information 318 associated with the built environment108. For example, the seating capacity of the event location may bedetermined as 200 based on the dynamic layout of the built environment108. Based on the extracted constraint information, the system 102 mayformulate an objective function of a constrained convex optimizationproblem for the route optimization. The system 102 may determine, fromthe group of users 112A, 112B, 112C, and 112D, a first cluster of userswho may be moving towards the first event location based on thepredicted event-specific actions for the group of users 112A, 112B,112C, and 112D and the received user preference information 316. Fromthe predicted paths for the group of users 112A, 112B, 112C, and 112D,the system 102 may select the one or more paths as the one or moreoptimal options for the first cluster of users by minimizing theformulation objective function.

At 310, navigation assistance may be provided. The system 102 maygenerate one or more navigation suggestions based on the selected one ormore paths (at 308). For example, for users who may be moving towardsthe event location of the poster session, a navigation suggestion may begenerated as “Turn right and go up one floor using the elevator”. Thesystem 102 may control one or more display devices 106 (such as adisplay device 328A or a display device 328B) to display the generatedone or more navigation suggestions. In one or more embodiments, thesystem 102 may generate and display the navigation suggestion for a useron a personal display device (such as a smartphone or a smartwatch).Such a suggestion may or may not be personalized for the user.Additionally, or alternatively, if a display device, such as a digitalsignage is installed at a suitable location in the built environment108, the system 102 may generate and display a navigation suggestion fora moving crowd of users on the digital signage.

In one or more embodiments, the one or more display devices 106 may beplaced in the built environment 108 at key locations where user densityat a given point in time may be predicted to be higher than a setthreshold. For example, one of such key locations may be an elevatorlobby where crowds may assemble to move to a particular floor of thebuilt environment 108. As an individual user or a crowd of users maymove towards an event location, the system 102 may control the one ormore display devices 106 to display updated navigation suggestions. Inone or more embodiments, the system 102 may display, on the one or moredisplay devices 106, a real-time seating capacity associated with theevent location along with the navigation suggestions. This may allowusers to decide or change their individual preferences or event-specificactions.

In described embodiments, both the graph-structured recurrent neuralnetwork 322 and the imitative decision learning framework 326 may beinclude one or more variants artificial neural networks. Herein, theartificial neural network may be considered as a computational networkor a system of artificial neurons as nodes, which may be arranged in theN number of layers. The N number of layers of the artificial neuralnetwork may include an input layer, a plurality of intermediate layers(also referred to as hidden layers), and one or more output layers. Eachlayer of the N number of layers may include one or more nodes(artificial neurons). Outputs of all nodes in the input layer may becoupled to at least one node of the plurality of intermediate layers.Similarly, inputs of each intermediate layer may be coupled to outputsof at least one node in other layers of the artificial neural network.Outputs of each intermediate layer may be coupled to inputs of at leastone node in other layers of the artificial neural network. Node(s) inthe one or more output layers may receive inputs from at least oneintermediate layer to output a result. The number of layers and thenumber of nodes in each layer may be determined from hyper-parameters ofthe artificial neural network. Such hyper-parameters may be set beforeor while training the artificial neural network on a training dataset.

Each node of the artificial neural network may correspond to amathematical function (e.g., a sigmoid function or a rectified linearunit) with a set of network parameters, tunable during training of theartificial neural network. The set of network parameters may include,for example, a weight parameter, a regularization parameter, and thelike. Each node may implement a mathematical function to compute anoutput based on one or more inputs from nodes in other layer(s) (e.g.,previous layer(s)) of the artificial neural network. All or some of thenodes of the artificial neural network may correspond to same or adifferent mathematical function.

In training of the artificial neural network, one or more networkparameters of each node of the artificial neural network may be updatedbased on whether an output of an output layer for a given input (fromthe training dataset) matches a correct result based on a loss functionfor the artificial neural network. The above process may be repeated forsame or a different input till a minima of loss function is achieved,and a training error is minimized. Several methods for training areknown in art, for example, gradient descent, stochastic gradientdescent, batch gradient descent, gradient boost, meta-heuristics, andthe like.

The artificial neural network may include electronic data, which may beimplemented, for example, as a software component, and may rely on codedatabases, libraries, external scripts, or other logic or instructionsfor execution by a processing device, such as by the system 102 of FIG.1 or FIG. 2. The artificial neural network may include codes configuredto enable a computing device or system, such as the system 102 toperform one or more operations for prediction or event-specific actionsor (most probable) paths for the group of users 112A, 112B, 112C, and112D in the built environment 108. Additionally, or alternatively, theartificial neural network may be implemented using hardware, including,but not limited to a processor, a microprocessor (e.g., to perform orcontrol performance of one or more operations), a field-programmablegate array (FPGA), or an application-specific integrated circuit (ASIC).Alternatively, in some embodiments, the artificial neural network may beimplemented using a combination of both hardware and softwarecomponents.

FIG. 4 is a flowchart of an example method of providing navigationsuggestions in a built environment, according to at least one embodimentdescribed in the present disclosure. FIG. 4 is explained in conjunctionwith elements from FIG. 1, FIG. 2 and FIG. 3. With reference to FIG. 4,there is shown a flowchart 400. The method illustrated in the flowchart400 may start at 402 and may be performed by any suitable system,apparatus, or device, such as by the system 102 of FIG. 1 or FIG. 2.

At 402, the user information 312, including the motion information 314associated with the group of users 112A, 112B, 112C, and 112D in thebuilt environment 108 and the user preference information 316 associatedwith the group of events hosted in the built environment 108 may bereceived. In one or more embodiments, the system 102 may be configuredto receive the user information 312.

At 404, the layout information 318 associated with the built environment108 may be received. In one or more embodiments, the system 102 may beconfigured to receive the layout information 318 which includes a staticphysical layout 318A of the built environment 108 and a dynamic layoutindicative of user density at a plurality of locations in the builtenvironment 108.

At 406, the schedule information 320 associated with the group of eventsmay be received. In one or more embodiments, the system 102 may beconfigured to receive the schedule information 320. The scheduleinformation 320 may include an event name for each event of the group ofevents, an event location of each event of the group of events in thebuilt environment 108, a seating capacity associated with each event ofthe group of events, and a time or duration associated with each eventof the group of events.

At 408, an event-specific action associated with each user of the groupof users 112A, 112B, 112C, and 112D in the built environment 108 may bepredicted. Such prediction may be based on the received user information312, the received layout information 318, and the received scheduleinformation 320. In one or more embodiments, the system 102 may beconfigured to predict the event-specific action associated with eachuser of the group of users 112A, 112B, 112C, and 112D in the builtenvironment 108.

At 410, a path for navigation towards a first event location associatedwith one of the group of events may be predicted for each user of thegroup of users 112A, 112B, 112C, and 112D. Such prediction may be basedon the predicted event-specific action of each user of the group ofusers 112A, 112B, 112C, and 112D and historical movement information 324of the group of users 112A, 112B, 112C, and 112D inside the builtenvironment 108. In one or more embodiments, the system 102 may beconfigured to predict, for each user of the group of users 112A, 112B,112C, and 112D, the path for navigation towards the first event locationassociated with one of the group of events.

At 412, one or more paths may be selected from the predicted paths forthe group of users 112A, 112B, 112C, and 112D as one or more optimaloptions for navigation towards the first event location. In one or moreembodiments, the system 102 may be configured to select, from thepredicted paths for the group of users 112A, 112B, 112C, and 112D, theone or more paths as the one or more optimal options for navigationtowards the first event location.

At 414, one or more navigation suggestions may be generated based on theselected one or more paths. In one or more embodiments, the system 102may be configured to generate the one or more navigation suggestionsbased on the selected one or more paths.

At 416, the one or more display devices 106 may be controlled to displaythe generated one or more navigation suggestions. In one or moreembodiments, the system 102 may be configured to control the one or moredisplay devices 106 to display the generated one or more navigationsuggestions. Control may pass to end.

Although the flowchart 400 is illustrated as discrete operations, suchas 402, 404, 406, 408, 410, 412, 414, and 416. However, in certainembodiments, such discrete operations may be further divided intoadditional operations, combined into fewer operations, or eliminated,depending on the particular implementation without detracting from theessence of the disclosed embodiments.

Various embodiments of the disclosure may provide a non-transitorycomputer-readable storage medium configured to store instructions that,in response to being executed, causes a system (such as the system 102)to perform operations. The operations may include receiving userinformation, including motion information associated with a group ofusers in a built environment and user preference information associatedwith a group of events hosted in the built environment. The operationsmay further include receiving layout information associated with thebuilt environment and receiving schedule information associated with thegroup of events. The operations may further include predicting anevent-specific action associated with each user of the group of users inthe built environment based on the received user information, thereceived layout information, the received schedule information. Theoperations may further include predicting, for each user of the group ofusers, a path for navigation towards a first event location associatedwith one of the group of events based on the predicted event-specificaction of each user of the group of users and historical movementinformation of the group of users inside the built environment. Theoperations may further include selecting, from the predicted paths forthe group of users, one or more paths as one or more optimal options fornavigation towards the first event location and generating one or morenavigation suggestions based on the selected one or more paths. Theoperations may further include controlling one or more display devicesto display the generated one or more navigation suggestions.

As used in the present disclosure, the terms “module” or “component” mayrefer to specific hardware implementations configured to perform theactions of the module or component and/or software objects or softwareroutines that may be stored on and/or executed by general purposehardware (e.g., computer-readable media, processing devices, etc.) ofthe computing system. In some embodiments, the different components,modules, engines, and services described in the present disclosure maybe implemented as objects or processes that execute on the computingsystem (e.g., as separate threads). While some of the system and methodsdescribed in the present disclosure are generally described as beingimplemented in software (stored on and/or executed by general purposehardware), specific hardware implementations or a combination ofsoftware and specific hardware implementations are also possible andcontemplated. In this description, a “computing entity” may be anycomputing system as previously defined in the present disclosure, or anymodule or combination of modulates running on a computing system.

Terms used in the present disclosure and especially in the appendedclaims (e.g., bodies of the appended claims) are generally intended as“open” terms (e.g., the term “including” should be interpreted as“including, but not limited to,” the term “having” should be interpretedas “having at least,” the term “includes” should be interpreted as“includes, but is not limited to,” etc.).

Additionally, if a specific number of an introduced claim recitation isintended, such an intent will be explicitly recited in the claim, and inthe absence of such recitation no such intent is present. For example,as an aid to understanding, the following appended claims may containusage of the introductory phrases “at least one” and “one or more” tointroduce claim recitations. However, the use of such phrases should notbe construed to imply that the introduction of a claim recitation by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should be interpreted to mean “at least one”or “one or more”); the same holds true for the use of definite articlesused to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitationis explicitly recited, those skilled in the art will recognize that suchrecitation should be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, means at least two recitations, or two or more recitations).Furthermore, in those instances where a convention analogous to “atleast one of A, B, and C, etc.” or “one or more of A, B, and C, etc.” isused, in general such a construction is intended to include A alone, Balone, C alone, A and B together, A and C together, B and C together, orA, B, and C together, etc.

Further, any disjunctive word or phrase presenting two or morealternative terms, whether in the description, claims, or drawings,should be understood to contemplate the possibilities of including oneof the terms, either of the terms, or both terms. For example, thephrase “A or B” should be understood to include the possibilities of “A”or “B” or “A and B.”

All examples and conditional language recited in the present disclosureare intended for pedagogical objects to aid the reader in understandingthe present disclosure and the concepts contributed by the inventor tofurthering the art and are to be construed as being without limitationto such specifically recited examples and conditions. Althoughembodiments of the present disclosure have been described in detail,various changes, substitutions, and alterations could be made heretowithout departing from the spirit and scope of the present disclosure.

What is claimed is:
 1. A method, comprising: receiving user informationcomprising motion information associated with a group of users in abuilt environment and user preference information associated with agroup of events hosted in the built environment; receiving layoutinformation associated with the built environment; receiving scheduleinformation associated with the group of events; predicting anevent-specific action associated with each user of the group of users inthe built environment based on the received user information, thereceived layout information, the received schedule information;predicting, for each user of the group of users, a path for navigationtowards a first event location associated with one of the group ofevents based on the predicted event-specific action of each user of thegroup of users and historical movement information of the group of usersinside the built environment; selecting, from the predicted paths forthe group of users, one or more paths as one or more optimal options fornavigation towards the first event location; generating one or morenavigation suggestions based on the selected one or more paths; andcontrolling one or more display devices to display the generated one ormore navigation suggestions.
 2. The method according to claim 1, furthercomprising controlling a plurality of motion trackers at a correspondingplurality of locations inside the built environment to collect themotion information associated with the group of users in the builtenvironment.
 3. The method according to the claim 2, further comprisingreceiving, from the plurality of motion trackers at the correspondingplurality of locations inside the built environment, a plurality ofimages as the motion information of the group of users.
 4. The methodaccording to claim 1, wherein the layout information comprises a staticphysical layout of the built environment and a dynamic layout indicativeof user density at a plurality of locations in the built environment. 5.The method according to claim 1, wherein the event-specific action foreach user of the group of users indicates a maximum likelihood ofmovement of a corresponding user towards a location in the builtenvironment.
 6. The method according to claim 1, wherein the receivedschedule information comprises an event name for each event of the groupof events, an event location of each event of the group of events in thebuilt environment, a seating capacity associated with each event of thegroup of events, and a time or duration associated with each event ofthe group of events.
 7. The method according to claim 1, furthercomprising: providing the received user information, the received layoutinformation, the received schedule information as input to agraph-structured recurrent neural network, wherein the graph-structuredrecurrent neural network is a pre-trained neural network in which nodesare configured for user detection and edges are configured to learnspatial or temporal user-to-user interactions and user-to-objectinteractions in the built environment, and the graph-structuredrecurrent neural network is configured to select, for each user of thegroup of users, an action label having a maximum likelihood from among aplurality of action labels of an action space based on the input; andpredicting the event-specific action of each user of the group of usersin the built environment based on selected action label.
 8. The methodaccording to claim 1, further comprising: providing the predictedevent-specific actions of the group of users and the historical movementinformation of the group of users as input to an imitative decisionlearning framework, wherein the imitative decision learning framework isconfigured to: extracting a distribution of latent decisions associatedwith the group of users based on the input; and generating a policy forpredicting the path for the navigation towards the first event locationfor each user of the group of users based on the input and extracteddistribution of latent decisions; and predicting, for each user of thegroup of users, the path for the navigation towards the first eventlocation associated with one of the group of events based on thegenerated policy.
 9. The method according to claim 1, furthercomprising: determining whether a first path of the predicted paths fora first user of the group of users coincides with a second path of thepredicted paths for a second user of the group of users; and selectingthe first path as an optimal option of the one or more optimal optionsfor both the first user and the second user for the navigation towardsthe first event location.
 10. The method according to claim 1, furthercomprising: extracting constraint information comprising seatingcapacity information associated with the first event location in thebuilt environment based on the received layout information associatedwith the built environment; formulating an objective function of aconstrained convex optimization problem for route optimization based onthe extracted constraint information; determining, from the group ofusers, a first cluster of users who are moving towards the first eventlocation based on the predicted event-specific actions for the group ofusers and the received user preference information; and selecting, fromthe predicted paths for the group of users, the one or more paths as theone or more optimal options for the first cluster of users to navigatetowards the first event location by minimizing the formulation objectivefunction.
 11. A non-transitory computer-readable storage mediumconfigured to store instructions that, in response to being executed,causes a system to perform operations, the operations comprising:receiving user information comprising motion information associated witha group of users in a built environment and user preference informationassociated with a group of events hosted in the built environment;receiving layout information associated with the built environment;receiving schedule information associated with the group of events;predicting an event-specific action associated with each user of thegroup of users in the built environment based on the received userinformation, the received layout information, the received scheduleinformation; predicting, for each user of the group of users, a path fornavigation towards a first event location associated with one of thegroup of events based on the predicted event-specific action of eachuser of the group of users and historical movement information of thegroup of users inside the built environment; selecting, from thepredicted paths for the group of users, one or more paths as one or moreoptimal options for navigation towards the first event location;generating one or more navigation suggestions based on the selected oneor more paths; and controlling one or more display devices to displaythe generated one or more navigation suggestions.
 12. The non-transitorycomputer-readable storage medium according to claim 11, wherein theoperations further comprises controlling a plurality of motion trackersat a corresponding plurality of locations inside the built environmentto collect the motion information associated with the group of users inthe built environment.
 13. The non-transitory computer-readable storagemedium according to claim 12, wherein the operations further comprisesreceiving, from the plurality of motion trackers at the correspondingplurality of locations inside the built environment, a plurality ofimages as the motion information of the group of users.
 14. Thenon-transitory computer-readable storage medium according to claim 11,wherein the layout information comprises a static physical layout of thebuilt environment and a dynamic layout indicative of user density at aplurality of locations in the built environment.
 15. The non-transitorycomputer-readable storage medium according to claim 11, wherein thereceived schedule information comprises an event name for each event ofthe group of events, an event location of each event of the group ofevents in the built environment, a seating capacity associated with eachevent of the group of events, and a time or duration associated witheach event of the group of events.
 16. The non-transitorycomputer-readable storage medium according to claim 11, wherein theoperations further comprises: providing the received user information,the received layout information, the received schedule information asinput to a graph-structured recurrent neural network, wherein thegraph-structured recurrent neural network is a pre-trained neuralnetwork in which nodes are configured for user detection and edges areconfigured to learn spatial or temporal user-to-user interactions anduser-to-object interactions in the built environment, and thegraph-structured recurrent neural network is configured to select, foreach user of the group of users, an action label having a maximumlikelihood from among a plurality of action labels of an action spacebased on the input; and predicting the event-specific action of eachuser of the group of users in the built environment based on selectedaction label.
 17. The non-transitory computer-readable storage mediumaccording to claim 11, wherein the operations further comprises:providing the predicted event-specific actions of the group of users andthe historical movement information of the group of users as input to animitative decision learning framework, wherein the imitative decisionlearning framework is configured to: extracting a distribution of latentdecisions associated with the group of users based on the input; andgenerating a policy for predicting the path for the navigation towardsthe first event location for each user of the group of users based onthe input and extracted distribution of latent decisions; andpredicting, for each user of the group of users, the path for thenavigation towards the first event location associated with one of thegroup of events based on the generated policy.
 18. The non-transitorycomputer-readable storage medium according to claim 11, wherein theoperations further comprises: determining whether a first path of thepredicted paths for a first user of the group of users coincides with asecond path of the predicted paths for a second user of the group ofusers; and selecting the first path as an optimal option of the one ormore optimal options for both the first user and the second user for thenavigation towards the first event location.
 19. The non-transitorycomputer-readable storage medium according to claim 11, wherein theoperations further comprises: extracting constraint informationcomprising seating capacity information associated with the first eventlocation in the built environment based on the received layoutinformation associated with the built environment; formulating anobjective function of a constrained convex optimization problem forroute optimization based on the extracted constraint information;determining, from the group of users, a first cluster of users who aremoving towards the first event location based on the predictedevent-specific actions for the group of users and the received userpreference information; and selecting, from the predicted paths for thegroup of users, the one or more paths as the one or more optimal optionsfor the first cluster of users to navigate towards the first eventlocation by minimizing the formulation objective function.
 20. A system,comprising: a processor configured to: receive user informationcomprising motion information associated with a group of users in abuilt environment and user preference information associated with agroup of events hosted in the built environment; receive layoutinformation associated with the built environment; receive scheduleinformation associated with the group of events; predict anevent-specific action associated with each user of the group of users inthe built environment based on the received user information, thereceived layout information, the received schedule information; predict,for each user of the group of users, a path for navigation towards afirst event location associated with one of the group of events based onthe predicted event-specific action of each user of the group of usersand historical movement information of the group of users inside thebuilt environment; select, from the predicted paths for the group ofusers, one or more paths as one or more optimal options for navigationtowards the first event location; generate one or more navigationsuggestions based on the selected one or more paths; and control one ormore display devices to display the generated one or more navigationsuggestions.